A Softened Formulation of Inductive Learning and Its Use for Coronary Disease Data
نویسندگان
چکیده
We present an improved inductive learning method to derive classification rules that correctly describe most of the examples belonging to a class and do not describe most of the examples not belonging to this class. The problem is represented as a modification of the set covering problems solved by a genetic algorithm. Its is employed to medical data on coronary disease, and the results seem to be encouraging.
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